The most difficult thing is the decision to act. And the rest is merely tenacity.

The secret of high-impact business is early preparation. 高效商务,赢在未雨绸缪. Early and best preparation is the secret of all successes. The most difficult thing is the decision to act, the rest if merely tenacity. 最难的是下定决心行动,其余的只需坚持不懈. We are often told tha

 H - Difficult Decision Time Limit:1000MS     Memory Limit:65536KB     64bit IO Format:%I64d & %I64u Submit Status Practice URAL 1507 Description Often, when a decision about investing in a new business must be taken, a New Russian has to estimate

1507. Difficult Decision Time limit: 1.0 second Memory limit: 64 MB Often, when a decision about investing in a new business must be taken, a New Russian has to estimate quickly whether a certain project will be a success or not. Leading economists h

题目链接:http://acm.timus.ru/problem.aspx?space=1&num=1507 Often, when a decision about investing in a new business must be taken, a New Russian has to estimate quickly whether a certain project will be a success or not. Leading economists have recently

译的不好,还请见谅... 大部分内容来自wiki decision theory决策理论部分: Normative and descriptive decision theory 规范和描述性决策理论 规范或规范的决策理论关心的是确定最好的决定(在实践中,有些情况下,"最好"的不一定是最大,最优可能还包括值除了最大,但在特定或近似范围),假设一个理想的决策者充分了解,能够准确无误地计算,完全理性的.这说明性的方法的实际应用(人们应该做出决定)决策分析,旨在发现工具,方法和软件帮助人们做

Chapter 1.5 : Decision Theory Chapter 1.5 : Decision Theory Christopher M. Bishop, PRML, Chapter 1 Introdcution 1. PRML所需要的三论: Probability theory: provides us with a consistent mathematical framework for quantifying and manipulating uncertainty. Deci

http://www.cnblogs.com/joneswood/archive/2012/03/04/2379615.html 1.      什么是Treelink Treelink是阿里集团内部的叫法,其学术上的名称是GBDT(Gradient Boosting Decision Tree,梯度提升决策树).GBDT是“模型组合+决策树”相关算法的两个基本形式中的一个,另外一个是随机森林(Random Forest),相较于GBDT要简单一些. 1.1    决策树 应用最广的分类算法之一

 介绍 我们有一些历史数据: record id\attributes A B C Result 1 a1 b1 c1 Good 2 a2 b2 c1 Bad 3 a1 b3 c2 Good 根据这些数据,我们想回答: If we got a record 4, that is 'a1,b1,c2',  is record4 Good or Bad? 解决方法 我们要分别考量record4的三个属性的先验(Priori)结果.那首先考虑属性C吧. C / \ c1(Subset1: Good-1

[转载自:http://www.cnblogs.com/LeftNotEasy/archive/2011/03/07/random-forest-and-gbdt.html] 前言 决策树这种算法有着很多良好的特性,比如说训练时间复杂度较低,预测的过程比较快速,模型容易展示(容易将得到的决策树做成图片展示出来)等.但是同时,单决策树又有一些不好的地方,比如说容易over-fitting,虽然有一些方法,如剪枝可以减少这种情况,但是还是不够的. 模型组合(比如说有Boosting,Bagging等